加强自动驾驶汽车在混合自动驾驶交通交叉点的决策:使用可解释分类器进行比较研究
Erika Ziraldo1, Megan Emily Govers1, Michele Oliver1
1School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada.
Sensors (Basel, Switzerland)
|June 27, 2024
概括
自动驾驶汽车 (AV) 可以通过使用车辆对车辆 (V2V) 通信来提高混合交通环境中的安全性. 这些数据有助于在十字路口进行优先决策,减少保守的驾驶行为和潜在的碰撞.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 运输工程 运输工程
背景情况:
- 过渡到自动驾驶道路涉及混合自动驾驶交通的时期,为自动驾驶汽车 (AV) 提出了挑战.
- 在复杂的场景中,自动驾驶汽车的保守驾驶行为可能会导致拥堵和与人类驾驶员的碰撞.
- 开发复杂的决策模型对于安全高效的混合自主导航至关重要.
研究的目的:
- 将时间序列森林 (TSF) 的性能与最先进的模型进行比较,用于混合自主交通中的优先分类任务.
- 为了评估车辆对车辆 (V2V) 数据,除了AV传感器数据,对分类准确性和模型复杂性的影响.
- 在模拟的危险交叉场景中评估这些模型的有效性.
主要方法:
- 使用全车驾驶模拟器,收集信号和停车标志控制的交叉路口对左转危险的反应.
- 采用了可解释的多变量时间序列分类器,时间序列森林 (TSF) 和另外两个最先进的模型.
- 使用数据集与AV传感器收集的特征和AV传感器/V2V传输特征组合的数据集进行分类性能比较.
主要成果:
- 在信号和停止信号控制交叉点数据集上,TSF表现相似,所有模型在信号数据集上表现更好.
- 整合V2V数据略有提高了整体准确性,并显著改善了停车标志控制场景的真正阳性率.
- V2V数据集成减少了所选特征的数量,降低了模型复杂度,同时保持了准确性.
结论:
- V2V数据集成提高了AVS在混合交通环境中的优先级分类模型的性能.
- 使用V2V数据可能会减少对过度保守的AV驾驶策略的需求,改善交通流量.
- 这种方法为更安全,更有效的自主导航提供了一条途径,而不会影响避免碰撞.
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